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Hierarchical shrinkage priors for dynamic regressions with many predictors

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  • KOROBILIS, Dimitris

    ()
    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

Abstract

This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using this single hierarchical Bayes formulation. Using 129 U.S. macroeconomic quarterly variables for the period 1959 – 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results show that for particular data series hierarchical shrinkage dominates factor model forecasts, and hence is a valuable addition to existing methods for handling large dimensional data.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2011021.

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Date of creation: 01 May 2011
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Handle: RePEc:cor:louvco:2011021

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Keywords: forecasting; shrinkage; factor model; variable selection; Bayesian LASSO;

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  1. Koop, Gary & Korobilis, Dimitris, 2010. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers 2010-113, Scottish Institute for Research in Economics (SIRE).
  2. Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December.
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  4. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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  7. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
  8. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
  9. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.
  10. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768.
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  17. Artin Armagan & Russell Zaretzki, 2010. "Model selection via adaptive shrinkage with t priors," Computational Statistics, Springer, vol. 25(3), pages 441-461, September.
  18. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320.
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Cited by:
  1. Gefang, Deborah, 2014. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage," International Journal of Forecasting, Elsevier, vol. 30(1), pages 1-11.
  2. Huang, Y-F., 2012. "Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach," MPRA Paper 41933, University Library of Munich, Germany.
  3. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
  4. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2013. "Forecasting the US Real Private Residential Fixed Investment Using Large Number of Predictors," Working Papers 201348, University of Pretoria, Department of Economics.
  5. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, 03.
  6. Nombulelo Gumata, Alain Kabundi and Eliphas Ndou, 2013. "Important Channels of Transmission Monetary Policy Shock in South Africa," Working Papers 375, Economic Research Southern Africa.
  7. Jiahan Li & Ilias Tsiakas & Wei Wang, 2014. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Working Paper Series 05_14, The Rimini Centre for Economic Analysis.
  8. Goodness C. Aye & Rangan Gupta & Stephen M. Miller & Mehmet Balcilar, 2014. "Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors," Working papers 2014-10, University of Connecticut, Department of Economics.
  9. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  10. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
  11. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
  12. Dimitris Korobilis, 2014. "Data-based priors for vector autoregressions with drifting coefficients," Working Papers 2014_04, Business School - Economics, University of Glasgow.
  13. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.

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